{"id":"W6905614257","doi":"10.15468/dl.dty2re","title":"Occurrence Download","year":2025,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Range (aeronautics); Set (abstract data type); Identification (biology); Download","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009074943,0.002122439,0.001347408,0.004407784,0.0009740966,0.002278993,0.002796069,0.00190977,0.1078481],"category_scores_gemma":[0.005937263,0.0008401741,0.001224727,0.008422358,0.0004494686,0.00215664,0.002431921,0.00194684,0.1689358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001613922,"about_ca_system_score_gemma":0.002432766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02402134,"about_ca_topic_score_gemma":0.04157824,"domain_scores_codex":[0.9989311,0.0001388175,0.0001475916,0.0003625523,0.0002543065,0.0001656405],"domain_scores_gemma":[0.997652,0.0006029765,0.0002147466,0.000634769,0.0006280606,0.00026745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003155527,0.00001368092,0.0003862017,0.0004091808,0.00001228914,0.00001511642,0.00002194628,0.0001281385,0.0001131354,0.0003767096,0.997022,0.001470044],"study_design_scores_gemma":[0.00008708164,0.00001096354,0.001933071,0.0001560983,0.00001310189,0.00004751894,0.00007855213,0.0002501949,0.0002324971,0.0008810438,0.9962912,0.00001878403],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007100986,0.00003200162,0.00006286524,0.00004926417,0.0000156326,0.000007695175,0.9982931,0.0006772168,0.000791285],"genre_scores_gemma":[0.0001808798,0.0000319592,0.0002633623,0.00004791334,0.000003598247,0.00004088638,0.9988286,0.0001561899,0.0004466483],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.892152,"threshold_uncertainty_score":0.3607877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03761864349018661,"score_gpt":0.2888078212483628,"score_spread":0.2511891777581762,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}